Social networking sites are attracting hundreds of millions of users to share information online. One critical task for all of these users is to decided the right audience with which to share. The decision about the audience can be at a coarse level (e.g., deciding to share with everyone, friends of friends, or friends), or at a fine level (e.g., deciding to share with only some of the friends). Performing such controlled sharing tasks can be tedious and error-prone to most users. An active social networking user can have hundreds of contacts. Therefore, it can be difficult to pick the right subset of them to share with. Also, a user can create a lot of content, and each piece of it can be shared to a different audience.
In this dissertation, I perform an extensive study of the controlled sharing problem and propose and implement a series of novel tools that help social networking users better perform controlled sharing. I propose algorithms that automatically generate a recommended audience for both static profile items as well as real-time generated content. To help users better understand the recommendations, I propose a relationship explanation tool that helps users understand the relationship between a pair of friends. I perform extensive evaluations to demonstrate the efficiency and effectiveness of our tools. With our tools, social networking users can control sharing more accurately with less effort. Finally, I also study an existing controlled-sharing tool, namely the circle sharing tool for Google+. I perform extensive data analyses and examine the impact of friend groups sharing behaviors on the development of the social network.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/97999/1/ljfang_1.pd
